Predicting zip code-level vaccine hesitancy in US Metropolitan Areas using machine learning models on public tweets

• Published in: PLOS digital health 2022-04, Vol.1 (4), p.e0000021-e0000021
• Main Authors: Melotte, Sara, Kejriwal, Mayank
• Format: Article
• Language: English
• Subjects: Algorithms; Biology and Life Sciences; Cognition & reasoning; Computer and Information Sciences; COVID-19 vaccines; Datasets; Earth Sciences; Machine learning; Medicine and Health Sciences; Metropolitan areas; Pandemics; Physical Sciences; Polls & surveys; Postal codes; Privacy; Real time; Research and Analysis Methods; Social networks; Social Sciences; Vaccines
• ISSN: 2767-3170; 2767-3170
• DOI: 10.1371/journal.pdig.0000021

Although the recent rise and uptake of COVID-19 vaccines in the United States has been encouraging, there continues to be significant vaccine hesitancy in various geographic and demographic clusters of the adult population. Surveys, such as the one conducted by Gallup over the past year, can be useful in determining vaccine hesitancy, but can be expensive to conduct and do not provide real-time data. At the same time, the advent of social media suggests that it may be possible to get vaccine hesitancy signals at an aggregate level, such as at the level of zip codes. Theoretically, machine learning models can be learned using socioeconomic (and other) features from publicly available sources. Experimentally, it remains an open question whether such an endeavor is feasible, and how it would compare to non-adaptive baselines. In this article, we present a proper methodology and experimental study for addressing this question. We use publicly available Twitter data collected over the previous year. Our goal is not to devise novel machine learning algorithms, but to rigorously evaluate and compare established models. Here we show that the best models significantly outperform non-learning baselines. They can also be set up using open-source tools and software.